This document discusses various sampling designs and their characteristics. It describes probability sampling designs like simple random sampling which gives every unit an equal chance of selection. It also describes non-probability sampling designs like purposive sampling which involves deliberately choosing units. Specific probability designs discussed include systematic sampling, stratified sampling, cluster sampling, area sampling, and multi-stage sampling.

1. QURATULAIN MUGHAL
BATCH IV
DOCTOR OF PHYSICAL THERAPY
ISRA UNIVERSITY

2.  SAMPLE DESIGN
 CHARACTERISTICS OF A GOOD SAMPLE
DESIGN
 TYPES OF SAMPLING DESIGN
 TERMINOLOGIES

3.  A definite plan for obtaining a sample from a given
population
 It refers to the technique or the procedure the
researcher would adopt in selecting items for the
sample.

4. (a) Sample design must result in a truly
representative sample.
(b) Sample design must be such which results in a
small sampling error.
(c) Sample design must be viable in the context of
funds available for the research study.
(d) Sample design must be such so that systematic
bias can be controlled in a better way.
(e) Sample should be such that the results of the
sample study can be applied, in general, for the
universe with a reasonable level of confidence.

6.  PROBABILITY SAMPLING is based on the
concept of random selection
 NON-PROBABILITY SAMPLING is ‘non-random’
sampling
 When each sample element is drawn individually
from the population at large, then the sample so
drawn is known as ‘UNRESTRICTED SAMPLE
 whereas all other forms of sampling are covered
under the term ‘RESTRICTED SAMPLING’.

7.  Is that sampling procedure which does not afford
any basis for estimating the probability that each
item in the population has of being included in the
sample.
 Non-probability sampling is also known by
different names such as:
a. deliberate sampling
b. purposive sampling
c. judgement sampling.

8.  In this type of sampling, items for the sample are
selected deliberately by the researcher; his choice
concerning the items remains supreme.

9.  in small inquiries and researches by individuals,
this design may be adopted because of the
relative advantage of time and money inherent in
this method of sampling

10.  there is always the danger of bias entering into
this type of sampling technique.
 Sampling error in this type of sampling cannot be
estimated
 this sampling design in rarely adopted in large
inquires of importance

11.  Under quota sampling the interviewers are simply
given quotas to be filled from the different strata,
with some restrictions on how they are to be filled.

12.  This type of sampling is very convenient
 and is relatively inexpensive.

13.  also known as ‘random sampling’ or ‘chance
sampling’.
 Under this sampling design, every item of the
universe has an equal chance of inclusion in the
sample.
 a lottery method in which individual units are
picked up from the whole group not deliberately
but by some mechanical process.

14.  we can measure the errors of estimation
 the sample will have the same composition and
characteristics

15.  refers to that method of sample selection which
gives each possible sample combination an equal
probability of being picked up and each item in the
entire population to have an equal chance of being
included in the sample.
 This applies to SAMPLING WITHOUT
REPLACEMENT i.e., once an item is selected for
the sample, it cannot appear in the sample again

16.  is used less frequently in which procedure the
element selected for the sample is returned to the
population before the next element is selected.
 In such a situation the same element could appear
twice in the same sample before the second
element is chosen

17.  Probability sampling under restricted sampling
techniques, may result in complex random
sampling designs.
 Such designs may as well be called ‘mixed
sampling designs’ for many of such designs may
represent a combination of probability and non-
probability sampling procedures in selecting a
sample.

18. 1. Systematic sampling
2. Stratified sampling
3. Cluster sampling
4. Area sampling
5. Multi-stage sampling
6. Sequential sampling

19.  In systematic sampling only the first unit is
selected randomly and the remaining units of the
sample are selected at fixed intervals
 It is an easier and less costlier method of sampling
 can be conveniently used even in case of large
populations
 systematic sampling is used when lists of
population are available and they are of
considerable length.

20.  Under stratified sampling the population is divided
into several sub-populations
 that are individually more homogeneous than the
total population.
 the different sub-populations are called ‘strata’
and then we select items from each stratum to
constitute a sample.
 stratified sampling results in more reliable and
detailed information

21.  cluster sampling the total population is divided into
a number of relatively small subdivisions which
are themselves clusters of still smaller units and
then some of these clusters are randomly selected
for inclusion in the overall sample.
 samples are usually more reliable per unit cost.

22.  If clusters happen to be some geographic
subdivisions, in that case cluster sampling is
better known as area sampling.
 In other words, cluster designs, where the primary
sampling unit represents a cluster of units based
on geographic area, are distinguished as area
sampling.

23.  Multi-stage sampling is a further development of
the principle of cluster sampling.
 EXAMPLE:
 Suppose we want to investigate the working
efficiency of nationalised banks in PAKISTAN we
want to take a sample of few banks for this
purpose.

24. 1. The first stage is to select large primary sampling unit such as
states in a country.
 Then we may select certain districts and interview all banks in
the chosen districts. This would represent a two-stage
sampling design with the ultimate sampling units being clusters
of districts.
 If instead of taking a census of all banks within the selected
districts, we select certain towns and
 interview all banks in the chosen towns.
 This would represent a three-stage sampling design.
 If instead of taking a census of all banks within the selected
towns, we randomly sample banks from each selected town,
then it is a case of using a four-stage sampling plan. If we select
randomly at all stages, we will have what is known as ‘multi-
stage random sampling design’

25.  The ultimate size of the sample under this technique is not
fixed in advance, but is determined according to
mathematical decision rules on the basis of information
yielded as survey progresses.
 This is usually adopted in case of acceptance sampling
plan in context of statistical quality control.
 When a particular lot is to be accepted or rejected on the
basis of a single sample, it is known as single sampling
 when the decision is to be taken on the basis of two
samples, it is known as double sampling
 and in case the decision rests on the basis of more than
two samples but the number of samples is certain and
decided in advance, the sampling is known as multiple
sampling.

26.  But when the number of samples is more than two
but it is neither certain nor decided in advance,
this type of system is often referred to as
sequential sampling.